Viola-Jones algorithm: Haar-like features, how are the features extracted?
I would look at table 1 of the original paper. While you're reading the alogorithm, try to really focus on Step 2 when you get to it.

In summary, each feature is used to train it's own classifier. So in your example, the calculated features X1, X2, ... Xn you describe coorespond to apply some set of feature transforms f_1, f_2, ... f_n to a single image. This is a bit backwards from what actually happens. What the method really does is train a classifier for each feature. So if you had n features, you would have n classifiers. Then in an adaboost fashion, you upweight the classifier that performed the best. I.e, you are upweighting the classifier based soley on th best performing feature. You then repeat and re-weight all the classifiers until you reach convergence.